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High voltage transmission network is a mesh network Distribution networks are largely radial networks (Socio-economic-technical) Dynamic system with phenomena at many time- scales Milliseconds, seconds, minutes, hours, days, months, and years Geographically distributed yet tightly interconnected Electric energy storage is very expensive and nearly impossible Energy produced must equal energy consumed on a second-by-second basis – power balance A complex hierarchical distributed control system has evolved over the years to ensure stability and performance of the large scale networked power system Electric Grid Characteristics

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Power Balance Power balance – balance power generation and consumption on a second-by-second basis Main Approach: adjust supply to meet demand with reliability Natural uncertainty in consumption [load] Use of reserve capacity to manage uncertainty and contingencies Day-ahead – hourly schedules, one day ahead Real-time – 5 minute schedules, 15 minutes ahead Automatic generation control using system frequency Deregulation of the electricity sector – unique mix of engineering and economics

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Power output varies in all time frames: Annual Seasonal Daily Hours Minutes Seconds Variability of Wind and Solar Source: CAISO Intermittency, uncontrollability, and uncertainty - principal causes of difficulty at the operational level in integration of wind and solar into the grid.

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Uncertainty – reliable predictions of power output are hard, particularly day ahead Uncontrollability – power output cannot be controlled as desired Intermittency – even if we could predict perfectly, the power output is inherently variable Variable Generation captures all three aspects into a single phrase Variability – Three Distinct Issues

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Regulatory guidelines require periodic assessment and multi-year planning to ensure sufficient generation capacity to meet demand LOLP - 1 day in 10 years criterion (p=0.9997) Resource Adequacy (RA) requirements; capacity markets Particularly challenging in deregulated markets Capacity credit Nameplate capacity fraction for meeting RA requirements Several probabilistic analysis techniques for CC calculation What is the capacity credit of VG (wind)? ~PJM, MISO – 13%, NYISO – 10% (summer), SPP – 10%, E.ON – 8%, … Could be even less at deep penetration What are the impacts on power system planning? How much traditional generation can be displaced by VG? What happens at deep penetration of VG? Capacity Credit

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Renewable Integration Many large scale studies have been conducted in the last few years Eastern Wind Integration and Transmission Study (EWITS) Western Wind and Solar Integration Study (WWSIS) CAISO Integration Studies Integration of Variable Generation Task Force (IVGTF) NREL Studies, European projects, … General conclusions: With sufficient transmission capacity, we can go up to 20-30% renewable electricity with significant impacts on power systems operations – implications for: Markets, reserves, balancing areas, flexibility of production stack, fast ramping resources, storage, adjustable demand, …. This is the focus of our work

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Large simulation studies to estimate the impact of 20% and 33% RPS Load following reserves: 2,292 MW in 2006 - 3,207 MW in 2012 - 4,423 MW in 2020 Up regulation reserves 277 MW in 2006 - 512 MW in 2012 - 1,135 MW in 2020 Questions: Is there a more rigorous method for estimating the additional reserves? Are there techniques to reduce the need for these additional reserves? Will/should renewable producers be required to provide their own reserves? California Results

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Wind power is taken to be a discrete-time stochastic process Normalized to name-plate capacity: CDF of the wind power process: Time averaged CDF: Wind Power Model

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Two settlement market Wind producer offers a contract for constant power C in the DAM at price p Imbalance prices in the real-time market: q shortfall price, excess price Imbalance prices q and are taken to be random variables while p is assumed to be known Market Model Contract, C Wind power w Forward day-ahead market Real-time market power time 1T p (q,  )

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Same two settlement market as before At each time instant, we can choose to inject energy into or extract energy from the storage device to maximize the net profit: Optimal Contract with Storage S = net injection = P inj - P ext Storage operation policy = g

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Consider a collection of geographically dispersed VG producers. Intuition: Averaging can reduce variability Questions: Can a group of wind power producers increase their collective profits by aggregating and offering their power output as a single entity? What profit sharing policy will ensure that the individual producers cooperate? Benefits of Aggregation Bayens et al. CDC’2011

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Current: adjust the generation to meet random demand Future: adjust demand to meet random generation Flexible Demand: heating, air-conditioning, refrigeration, water heaters, EVs, … These are energy consumers, not power consumers Questions: How can we optimize aggregate and optimize flexibility of large numbers of individual flexible loads? How can sensing and communications be used for distributed control of flexible loads? What incentive and pricing mechanisms will be effective in getting consumers to participate in adjustable demand programs? How can these distributed resources be integrated into power system operations with large RG penetration? Paradigm Change

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Scenario: Large numbers of solar, wind, CHP, and micro-generators in the distribution system Adjustable demand, electric vehicles Storage Sensing, communications, computing, control (SG) Questions: What is the optimal, scalable, control and communications architecture to control such a large scale distributed power system? How can we do this while respecting the legacy centralized grid and minimize the need for additional reserves? What level of renewable penetration can be achieved in such a distributed scenario? Answer: GRIP: Grid with Intelligent Periphery Coordinated aggregation & control using smart grid sensing, communications, computation, and control Distributed Renewable Generation Bakken et al, SmartGridCom’2011